Due to their pollination services, honeybees are one of the most ecologically vital animals, being singlehandedly responsible for nearly 80% of global agricultural pollination [1]. However, in recent years, they have experienced large declines in populations, and as a survey reported roughly 50% of beekeepers in the US lost their honeybee colonies [2]. These losses are experienced globally due to a combination of many factors, including but not limited to habitat loss, pesticides, climate change, and other invasive species [3, 4]. One of the biggest factors attributed to the decline of honeybee colonies is the usage of pesticides, specifically neonicotinoids [3-6]. Neonicotinoid compounds have been used globally since their introduction in the early 1990s [4]. Studies have shown that neonicotinoids can have both sublethal and lethal effects on honeybees, depending on the dosages that they are exposed to, as neonicotinoids bind to nervous system receptors of honeybees [7]. These effects can range from behavior changes to altered motor functions [7-9]. Among the reported effects, one of the more significant ones is the effect of neonicotinoids on honeybee learning and memory [10, 11]. Additionally, there is a lack of availability for methods of monitoring of honeybee hives, essentially meaning that the only methods to track honeybee health are through obtrusive physical methods of inspection. This paper aims to develop a novel AI-based honeybee health assessment system, able to monitor beehives using the following functions: continuous temperature and humidity monitoring both inside and outside the hive, as well as video and audio recording to assess honeybee health as well as population. In addition, this system can be used for honeybee-related studies such as nutrition effects and evaluation on health, learning, and memory. To do this, four types of nutrition have been studied and their effects have been analyzed by a deep learning approach.